Prevalence and Predictors of Nonadherence to Direct Oral Anticoagulant Treatment in Patients with Atrial Fibrillation.

Background For most patients with newly diagnosed atrial fibrillation (AF), direct oral anticoagulants (DOACs) are preferred over vitamin K antagonists. However, there is concern that the lack of monitoring may impair therapy adherence and therefore the anticoagulant effect. Objective To assess 1-year DOAC nonadherence in patients with AF and a treatment indication of at least 1 year in the Dutch health care setting, and to identify predictors of nonadherence. Methods We performed a near-nationwide historical cohort study in patients with a novel DOAC indication for AF. Data were obtained from a pharmacy database, covering 65% of all outpatient prescriptions dispensed in the Netherlands. The 1-year nonadherence was assessed by the proportion of days covered; the threshold was set at <80%. Robust Poisson regression analyses were performed to identify predictors of nonadherence. Results A total of 46,211 patients were included and the 1-year nonadherence was 6.5%. We identified male sex (risk ratio [RR] 1.23, 95% confidence interval [CI]: 1.15-1.33), younger age (age ≥60 to <70 years: RR: 1.15, 95% CI: 1.00-1.33, age <60 years: RR: 2.22, 95% CI: 1.92-2.57; reference age ≥85 years), a reduced DOAC dose (RR: 1.10, 95% CI: 1.00-1.22), a twice-daily dosing regimen (RR: 1.21, 95% CI: 1.12-1.30), and treatment with apixaban (RR: 1.16, 95% CI: 1.06-1.26, reference rivaroxaban) or dabigatran (RR: 1.25, 95% CI: 1.14-1.37) as independent predictors of 1-year nonadherence. Conclusion One-year nonadherence to DOACs was low yet relevant in patients with AF newly prescribed a DOAC. Understanding the predictors for nonadherence may help identify patients at risk.

Chest CT in COVID-19 at the ED: Validation of the COVID-19 Reporting and Data System (CO-RADS) and CT Severity Score: A Prospective, Multicenter, Observational Study.

BACKGROUND: CT is thought to play a key role in coronavirus disease 2019 (COVID-19) diagnostic workup. The possibility of comparing data across different settings depends on the systematic and reproducible manner in which the scans are analyzed and reported. The COVID-19 Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) introduced by the Radiological Society of the Netherlands (NVvR) attempt to do so. However, this system has not been externally validated. RESEARCH QUESTION: We aimed to prospectively validate the CO-RADS as a COVID-19 diagnostic tool at the ED and to evaluate whether the CTSS is associated with prognosis. STUDY DESIGN AND METHODS: We conducted a prospective, observational study in two tertiary centers in The Netherlands, between March 19 and May 28, 2020. We consecutively included 741 adult patients at the ED with suspected COVID-19, who received a chest CT and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR (PCR). Diagnostic accuracy measures were calculated for CO-RADS, using PCR as reference. Logistic regression was performed for CTSS in relation to hospital admission, ICU admission, and 30-day mortality. RESULTS: Seven hundred forty-one patients were included. We found an area under the curve (AUC) of 0.91 (CI, 0.89-0.94) for CO-RADS using PCR as reference. The optimal CO-RADS cutoff was 4, with a sensitivity of 89.4% (CI, 84.7-93.0) and specificity of 87.2% (CI, 83.9-89.9). We found a significant association between CTSS and hospital admission, ICU admission, and 30-day mortality; adjusted ORs per point increase in CTSS were 1.19 (CI, 1.09-1.28), 1.23 (1.15-1.32), 1.14 (1.07-1.22), respectively. Intraclass correlation coefficients for CO-RADS and CTSS were 0.94 (0.91-0.96) and 0.82 (0.70-0.90). INTERPRETATION: Our findings support the use of CO-RADS and CTSS in triage, diagnosis, and management decisions for patients presenting with possible COVID-19 at the ED.

How We Evaluate Postgraduate Medical E-Learning: Systematic Review.

BACKGROUND: Electronic learning (e-learning) in postgraduate medical education has seen a rapid evolution; however, we tend to evaluate it only on its primary outcome or learning aim, whereas its effectiveness also depends on its instructional design. We believe it is important to have an overview of all the methods currently used to evaluate e-learning design so that the preferred method may be identified and the next steps needed to continue to evaluate postgraduate medical e-learning may be outlined. OBJECTIVE: This study aimed to identify and compare the outcomes and methods used to evaluate postgraduate medical e-learning. METHODS: We performed a systematic literature review using the Web of Science, PubMed, Education Resources Information Center, and Cumulative Index of Nursing and Allied Health Literature databases. Studies that used postgraduates as participants and evaluated any form of e-learning were included. Studies without any evaluation outcome (eg, just a description of e-learning) were excluded. RESULTS: The initial search identified 5973 articles, of which we used 418 for our analysis. The types of studies were trials, prospective cohorts, case reports, and reviews. The primary outcomes of the included studies were knowledge, skills, and attitude. A total of 12 instruments were used to evaluate a specific primary outcome, such as laparoscopic skills or stress related to training. The secondary outcomes mainly evaluated satisfaction, motivation, efficiency, and usefulness. We found 13 e-learning design methods across 19 studies (4% 19/418). The methods evaluated usability, motivational characteristics, and the use of learning styles or were based on instructional design theories, such as Gagne's instructional design, the Heidelberg inventory, Kern's curriculum development steps, and a scale based on the cognitive load theory. Finally, 2 instruments attempted to evaluate several aspects of a design, based on the experience of creating e-learning. CONCLUSIONS: Evaluating the effect of e-learning design is complicated. Given the diversity of e-learning methods, there are many ways to carry out such an evaluation, and probably, many ways to do so correctly. However, the current literature shows us that we have yet to reach any form of consensus about which indicators to evaluate. There is a great need for an evaluation tool that is properly constructed, validated, and tested. This could be a more homogeneous way to compare the effects of e-learning and for the authors of e-learning to continue to improve their product.

Digital vs face-to-face information provision in patient counselling for prenatal screening: A noninferiority randomized controlled trial.

OBJECTIVE: To evaluate face-to-face information provision in patient counselling for prenatal screening compared with two forms of digital information provision, namely, noninteractive instructional video or interactive video. METHOD: We performed a prospective, noninferiority, cluster-randomized controlled trial comparing face-to-face (usual care) with two forms of digital information provision (intervention) in counselling for prenatal screening. This study was performed in the Amsterdam UMC, the Netherlands, in 2017, and included women in the first trimester of pregnancy. Main outcomes were knowledge gained by the patient and counselling duration. We performed a noninferiority analysis. RESULTS: One hundred forty-one women were included, randomized, and analysed. The baseline characteristics were comparable. The intervention group was noninferior compared with the control group regarding the level of satisfaction. The knowledge grade difference was higher after using intervention, and the duration was significantly longer in the face-to-face group at 23 minutes versus 16 minutes. The addition of interaction with the video made no difference in any of the outcomes. CONCLUSION: Adding an instructional video to patient counselling is of added value to improve patient's knowledge and shorten time consumption of the counsellor, therefore possibly saving costs. But this form of counselling maintains the same level of satisfaction.